Metric-based approaches aim at constructing an embedding space to measure similarities between the labeled and unlabeled images. This line of approaches mainly depends on designing a suitable embedding space and a distance measurement method, which is simple and effective. Matching Network [1]} combines attention mechanism with LSTM to obtain the probability distribution belonging to the categories in the support set. Besides, it also proposes the episode training paradigm, which is widely employed in FSL. Relation Network [2]} employs deep convolution networks to replace the artificial distance function, which achieves promising performance. Snell \( et~al. \)  [3]} assumed that there are class prototypes in the embedding space for each class. They designed Prototypical Network, which averages the samples in the support set as class prototypes. The above three approaches have established the foundation of metric-based FSL. Many studies conduct incremental improvements based on them, such as [4]}, [5]}, [6]}, [7]}, [8]}.
